We compare our approach with previous face forgery detection methods on the FF++ dataset.
The results are listed in Table REF . Xception [1]} and Face X-ray [2]}
are currently state-of-the-art image-based detection methods. Our method outperforms them on various versions of FF++ dataset
in terms of AUC, pAUC\(_{0.1}\) , and accuracy. In the most challenging c40 version, we achieve a 6.4\(\%\) , 15.8\(\%\) , and 2.86\(\%\)  improvement in AUC, pAUC\(_{0.1}\) , and accuracy respectively.
Although two-branch [3]} is a video-based detection method, we still
surpass it, especially in the c40 version.
The results demonstrate the effectiveness and superiority of our framework.
